Tsai et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:33 http://jwcn.eurasipjournals.com/content/2011/1/33
RESEARCH
Open Access
Optimal modulation and coding scheme allocation of scalable video multicast over IEEE 802.16e networks Chia-Tai Tsai, Rong-Hong Jan* and Chien Chen
Abstract With the rapid development of wireless communication technology and the rapid increase in demand for network bandwidth, IEEE 802.16e is an emerging network technique that has been deployed in many metropolises. In addition to the features of high data rate and large coverage, it also enables scalable video multicasting, which is a potentially promising application, over an IEEE 802.16e network. How to optimally assign the modulation and coding scheme (MCS) of the scalable video stream for the mobile subscriber stations to improve spectral efficiency and maximize utility is a crucial task. We formulate this MCS assignment problem as an optimization problem, called the total utility maximization problem (TUMP). This article transforms the TUMP into a precedence constraint knapsack problem, which is a NP-complete problem. Then, a branch and bound method, which is based on two dominance rules and a lower bound, is presented to solve the TUMP. The simulation results show that the proposed branch and bound method can find the optimal solution efficiently. Keywords: Adaptive modulation and coding, Branch and bound algorithm, IEEE 802.16e, Resource allocation, Scalable video coding
1 Introduction With the popularity of wireless networks, the need for network bandwidth is growing rapidly. In order to provide high quality service, various categories of broadband wireless network techniques, e.g., IEEE 802.16e (or WiMAX, Worldwide Interoperability for Microwave Access) and 3GPP LTE, have been proposed. Among these techniques, IEEE 802.16e is an emerging network technique and has been deployed in many metropolises (e.g., Chicago, Las Vegas, Seattle, Taipei and so forth [1,2]). It provides mobile users with a high data rate (up to 75 Mbps) and a large coverage range (up to a radius of 10 miles) [3-5]. In addition, it also enables new classes of real-time video services, such as IPTV services, video streaming services, and live TV telecasts, which require a large transmission bandwidth, and need identical content to be delivered to several mobile stations. The most efficient way to provide such services is to use wireless multicasting, sending one copy of the video stream to multiple subscriber stations via a * Correspondence:
[email protected] Department of Computer Science National Chiao Tung University 1001 University Road, Hsinchu 300, Taiwan
shared multicast channel, instead of sending multiple copies via several dedicated channels [6]. In this way, wireless multicasting can reduce bandwidth consumption significantly. IEEE 802.16e supports a variety of modulation and coding schemes (MCSs), such as QPSK, 16QAM, and 64QAM, and allows these schemes to change on a burstby-burst basis per link, depending on channel conditions [3-5]. Adaptive modulation and coding (AMC) is a term used in wireless communications to denote the matching of the modulation and coding to the channel condition for each subscriber station. It is widely applied to wireless networks. For example, the IEEE 802.16e base station (BS) can assign an appropriate MCS to each mobile subscriber station (MSS) based on its channel quality. This can be done by having the MSS advise its downlink channel quality indicator to the BS. The BS scheduler can take into account the channel quality of the MSS and assign an appropriate MCS for each of them so that the throughput is maximized. Owing to the mobility (i.e., the ability to move within the coverage area) of the MSS, the signal-to-noise ratio
© 2011 Tsai et al; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Tsai et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:33 http://jwcn.eurasipjournals.com/content/2011/1/33
(SNR) from the BS may become degraded (i.e., the MSS could be in poor channel condition at some time). The adaptation strategy for the MSS with the worst channel condition will cause the data rate to be low, especially when the multicast group size is large [7]. For example, as shown in Figure 1, the BS chooses QPSK, the most conservative and robust MCS, to accommodate all MSSs in the multicast group, even if there are some MSSs (e.g., MSS1, MSS2, and MSS3) that can be accommodated with a higher data rate MCS (e.g., 16QAM). That is, the multicast data rate is determined by the MSS which has the worst channel condition (e.g. MSS 4). As a result, the spectral efficiency tends to be poor. The scalable video coding (SVC) scheme [8] allows for the delivery of a decodable and presentable quality of the video depending on the MSS’ channel quality. The SVC scheme divides a video stream into one base layer and several enhancement layers [8]. The base layer provides a basic video quality, frame rate, and resolution of the video, and the enhancement layers can refine the video quality, frame rate, and resolution. Figure 2 shows the video quality under various combinations of video layers. The more video layers an MSS receives, the better video quality it can get. In this article, we apply the utility [9,10] to measure the satisfaction degree of the video quality that the MSS received. In wireless networks, because the air resources are limited and shared by all receivers, organizing the layering structure of a video stream and assigning the appropriate MCS for each video layer to maximize the total utility is a crucial task [11-21]. Formally, the problem
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can be stated as follows: consider a video multicasting network having a scalable video stream V consisting of m video layers L = {l1, l2,..., lm} and adaptive MCS consisting of n MCSs {M1, M2, ..., Mn}. The BS chooses a layering structure (i.e., selecting a set of video layers L′ from L), which will multicast to the MSSs, and determines an appropriate MCS for each video layer in L′ such that the total utility is the maximized subject to a bandwidth constraint. In this article, we formulate the MCS assignment of the layering structure as a total utility maximization problem (TUMP). This article transforms the TUMP into a precedence constraint knapsack problem, which is a NP-complete problem [22]. The precedence-constraint knapsack problem is a generalization of the knapsack problem, which includes the constraint on the packed order of the items. For example, if item i precedes item j, then item j can only be packed into the knapsack if item i is already packed into the knapsack. Because the solution space of the problem TUMP consists of a large number of fruitless candidates, a branch and bound method which is based on two dominance rules and a lower bound is presented to solve the TUMP. The simulation results show that the proposed branch and bound method can find the optimal solution efficiently. Because the optimal solution can be found with just a little computation time, the proposed method is suitable for MCS assignment in a scalable video multicast over IEEE 802.16e networks. This article is organized as follows: In Section 2, we describe and formulate the TUMP problem. We
MSS 4
MSS 2 Video Stream l1 l2 ... lk
l1 l2 ... lm
Multicast Group MSS 3
Internet Video Server
MSS 1
BS
QPSK 16QAM
64QAM
Figure 1 The video multicast network environment over IEEE 802.16e networks.
Tsai et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:33 http://jwcn.eurasipjournals.com/content/2011/1/33
(a) Only one base layer
(b) One base layer and one enhancement layer
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(c) One base layer and two enhancement layers
Figure 2 The video quality for the MSS under various numbers of video layers (the video, foreman, is downloaded from the video trace library [27]). (a) Only one base layer. (b) One base layer and one enhancement layer. (c) One base layer and two enhancement layers.
transform the TUMP into a precedence constraint knapsack problem and propose a branch and bound method to solve the TUMP in Section 3. The experimental results are given in Section 4. Finally, we conclude this article in Section 5.
2. Problem description 2.1. Statement of the problem
In this article, we consider a video multicast network environment over an IEEE 802.16e network as shown in Figure 1. The MSSs can access the Internet through the BS. The ranging process occurs when an MSS joins the network and updates periodically; hence, the BS can obtain the link quality of each MSS [3-5]. Suppose that there is a set of MSSs joined to a multicast group and subscribing to a scalable video stream V consisting of m video layers L = {l1, l2,..., lm}. The video server delivers V to the BS through the Internet. The BS has n MCSs {M1, M2,..., Mn}. It takes each MSS’s channel quality and the number of available time slots into account before organizing the layering structure. If the number of available time slots is not large enough, then the BS has to choose a set of feasible video layers L′ from L and determine an appropriate MCS for each video layer in L′. Our goal is to maximize the total utility under a bandwidth constraint.
supports three MCSs QPSK, 16QAM, and 64QAM, i.e., M1 = QPSK, M2 = 16QAM, and M3 = 64QAM. Suppose that the MSS receives a set of video layers L′ = {l1, l2,..., lk, lx, ly,..., lz} from a BS where k + 1 21, node 4 is infeasible and gets killed (or bounded). By the same method, the algorithm chooses node 8 for branching (see Figure 8). Since h(9) = 24 > 21 and h(10) = 22 > 21, nodes 9 and 10 get killed. The algorithm finds the next node for branching from the live-node list. Since g (p), p = 11, 13, 19, 22, 23, which are smaller than or equal to LT = f(8) = 5.5, nodes 11, 13, 19, 22, and 32 are bounded. Now, the live-node list is empty and then
the algorithm will be terminated. The maximum utility answer node is node 8. It has a utility of 5.5. That is, the optimal solution is (x11 = 1, x21 = 1, x32 = 1). The video layers l1, l2, and l3 are selected to be delivered and are encoded by M1, M1, and M2, respectively.
f(1) = 0 h(1) = 0 1
f(1) = 0
g(2) = 7 x11 = 1
h(1) = 0 *
1
g(2) = 7 x11 = 1
f(2) = 2.8 h(2) = 8
g(32) = 2
32
g(19) = 4 g(13) = 4.6
3
2
22
2
g(3) = 7 x21 = 1
g(22) = 3
g(32) = 2 g(22) = 3
22
Figure 6 The algorithm chooses node 2 for branching.
32
13
19
Figure 7 The algorithm chooses node 3 for branching and the current optimal solution is (x11 = 1) and current total utility is z* = f(2) = 2.8.
Tsai et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:33 http://jwcn.eurasipjournals.com/content/2011/1/33
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f(1) = 0 h(1) = 0 1
g(2) = 7 x11 = 1 f(2) = 2.8 h(2) = 8
g(32) = 2 g(22) = 3 22
2
g(3) = 7 x21 = 1 f(3) = 4.9 h(3) = 16 3
32
g(19) = 4 g(13) = 4.6 13
19
g(11) = 5.5
g(4) = 7 g(8) = 5.8
x32 = 1 4
h(4) = 24
*
f(8) = 5.5 h(8) = 20
9
8
11
bounded node
10
h(9) = 24 h(10) = 22 Figure 8 The optimal solution of a video stream is (x11 = 1, x21 = 1, x32 = 1) and optimal total utility z* = f(8) = 5.5.
4.2. Experimental results
We have conducted simulations to demonstrate how effective the proposed mathematical model is. The simulation ran on a BS with 100 MSSs which were randomly placed within a cell. The coverage area of the BS was divided into six rings, P1, P2,..., and P6 as shown in Figure 9. Six types of MCS as in the IEEE 802.16e standard [3-5] were used (i.e., n = 6). The MSS in rings P1, P2,..., and P6 can be accommodated with MCS sets {M1, M2, M 3, M 4, M5 , M6 }, {M 1 , M 2 , M 3 , M 4, M 5},..., and {M 1 }, respectively. The video stream was divided into one base layer and six enhancement layers (i.e., m = 7). The utility function was assumed to be log-normally distributed due to the attenuation caused by shadowing or slow fading in the wireless communication. The shape parameter and the scale parameter of the utility function were set to 1.5 and 0.5, respectively (see Figure 3) [10]. Three assigning MCS methods were considered in the simulation:
1). The naive method: It chooses the highest MCS, which can be received by all MSSs in the multicast group, to encode the video layers, and allocates the available timeslots to the video layers one by one until the remaining timeslots cannot accommodate the next layer. 2). The uniform method [26]: It chooses the highest MCS, which can be received by all MSSs in the multicast group, to encode the base layer. Next, the uniform algorithm chooses the MCS which covers at least 60% of the MSSs in the multicast group to encode the enhancement layers. 3). The proposed method: It solves the TUMP problem to find the optimal MCS for each video layer by the branch and bound algorithm. The total utility values achieved by the naive method, the uniform method, and the proposed method are denoted by X naive , X uni , and X opt , respectively. The
Tsai et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:33 http://jwcn.eurasipjournals.com/content/2011/1/33
P6
P5
P4
P3
P2
P1
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BS
Figure 9 The coverage area of the BS with six rings.
comparisons among X naive , X uni , and X opt are made (shown in Figure 10). Each data point in Figure 10 is the average over 10 runs. The results show that the total utility values Xopt are greater than Xuni or Xnaive. The gaps among Xnaive, Xuni, and Xopt are larger when the available bandwidth is in the range of 1500-3000 timeslots/s. Figure 11 shows one sample of the simulation results for the optimal algorithm and the uniform algorithm with the number of available timeslots S = 2500. As shown in Figure 11a, for both algorithms, the MSS can receive more video layers when it is more close to the BS. However, the numbers of video layers delivered by the optimal algorithm to the MSS in all rings except ring P6 are greater
than or equal to the numbers of video layers delivered by the uniform algorithm. Similarly, from Figure 11b, it is noted that the utility values achieved by the optimal algorithm are greater than or equal to the values achieved by the uniform algorithm for all rings except ring P6. In this sample, the numbers of the MSSs for rings P1, P2, P3, P4, P5, and P6 were 3, 5, 42, 7, 10, and 33, respectively. The total utility achieved by the proposed algorithm was 40.92 ( = (3 + 5 + 42) × 0.71 + 7 × 0.47 + 10 × 0.18 + 33 × 0.01), while that achieved by the uniform algorithm was 34.53 ( = (3 + 5 + 42 + 7) × 0.47 + (10 + 33) × 0.18). The optimal algorithm shows its benefit. On the other hand, we also present the computational experiments to show the effectiveness of the branch and
100
Total Utility
80 60
Xopt Xopt Xuni Xuni Xnaive Xnaive
40
20 0 0
1500
2750
4000
5250
6500
Availabile timeslots per second Figure 10 The utility of the optimal solution, the uniform algorithm, and the naive algorithm with different available timeslots per second.
Tsai et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:33 http://jwcn.eurasipjournals.com/content/2011/1/33
1
Uniform Optimal
4 3 2 1
Utility for an MSS
Number of video layers
5
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0.8 0.6
Uniform Optimal
0.4 0.2 0
0 P1
P2
P3
P4
P5
P1
P6
P2
Ring
P3
P4
P5
P6
Ring
(a)
(b)
Figure 11 The number of video layers that an MSS can receive and the utility of an MSS under various rings when the available timeslots (S) equal to 2500.
bound algorithm. The real execution times of the algorithm depend on the number of video layers (m), the number of MCSs (n), and the number of available time slots (S). The experiments were conducted on a desktop PC with an Intel Core 2 Duo 1.6GHz processor and 2 GB memories. The operating system was Windows XP. The programs were coded in C and are available from the corresponding author upon request. The simulation also ran on a BS with 100 MSSs which were randomly placed. We assume the frame duration is
5 ms. Each MSS subscribes a scalable video, in which the video rate is 320 kbps (i.e., 1.6 kb per frame). The video rate is a measure of the rate of information content in a video stream. The video is divided equally across the number of video layers. The simulation results are summarized in Table 3 which includes the number of nodes generated, the number of computations of f(p), and the execution time (CPU time). Table 3 shows that Figure 5 appreciably reduces the number of nodes generated and the number of unnecessary tries for infeasible nodes. For
Table 3 The simulation results under various numbers of MCSs, video layers, and available time slots m
n=3 S
2
4
6
8
10
Computations of f(p)
n=6
Nodes generated
CPU time (μs)
S
Computations of f(p)
Nodes generated
CPU time (μs)
3
3
2 × 10
3
6
0.842
2 × 10
1
4
0.914
4 × 103
1
3
0.634
4 × 103
1
6
1.138
6 × 103
2
5
0.756
6 × 103
2
11
1.442
3
3
8 × 10
2
6
0.817
8 × 10
2
12
1.618
2 × 103
9
9
2.928
2 × 103
6
14
4.190
4 × 103
3
7
1.727
4 × 103
3
15
3.038
3
3.500
3
6 × 10
4
11
2.021
6 × 10
4
22
8 × 103
4
12
2.105
8 × 103
4
24
3.580
2 × 103
19
19
6.655
2 × 103
22
43
14.027
4 × 103
4
10
2.813
4 × 103
5
22
5.220
3
6.286
3
6 × 10
5
15
3.583
6 × 10
5
30
8 × 103
6
18
3.831
8 × 103
6
36
6.632
2 × 103
22
25
9.673
2 × 103
47
96
32.430
4 × 103
7
15
4.955
4 × 103
6
29
8.512
3
6 × 10
7
21
5.583
6 × 103
7
42
9.869
8 × 103
8
24
5.980
8 × 103
8
48
10.357
2 × 103
40
43
17.61
2 × 103
107
202
75.604
3
4 × 10
10
20
7.397
4 × 103
13
50
16.035
6 × 103
10
27
8.210
6 × 103
10
55
15.239
8.225
3
10
60
14.747
3
8 × 10
10
30
8 × 10
Tsai et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:33 http://jwcn.eurasipjournals.com/content/2011/1/33
example, if you apply an exhaustive search to a problem with size (m, n) = (10, 6), then the total number of nodes = 8008 ≈ 214 . However, the number of nodes is 10+6 6 generated by Figure 5 is only 202 < 28 (see Table 3) for (m, n, S) = (10, 6, 2 × 103) because we apply the branch and bound approach. It takes 107 tries to compute f(p) for obtaining the MCS assignment of the layering structure, which is much less than 8008. This means that the dominance rules (i.e., feasibility test and utility bound) can be employed to discard the most infeasible and unnecessary nodes before computing f(p). This reduces the computational time significantly. From Table 3, the computational time to determine the optimal MCS assignment for the video layering structure is less than 75.604 μs. The computational time is small enough. Thus, the branch and bound method is effective and suitable for BS to determine the video layering structure and MCS assignment for IEEE 802.16e network multicast.
5. Conclusion In this article, we consider an optimal MCS assignment problem which improves spectral efficiency and maximizes total utility for the scalable video multicast in IEEE 802.16e networks. We propose a branch and bound algorithm to find an optimal solution for this problem. In the experiment, it was shown that the proposed method performs well compared to the uniform method and the naïve method. The computation time of the proposed branch and bound algorithm is very small. Thus, our proposed method is suitable for BS to determine the video layering structure and the MCS assignment in the IEEE 802.16e network multicast. Because of the Doppler Effect, when MSS is moving, the MSS’s velocity causes a shift in the frequency of the signal transmitted along each signal path. It causes fast fading of the received signal for MSS. Thus, the BS will encode the video layers by the more conservative and robust MCS for the moving MSS. Therefore, the video quality for MSS when it stands at a point is better than that when it moves within the same region. Looking ahead, considering the mobility of MSS for the MCS assignment problem might be an interesting future study. Abbreviations AMC: adaptive modulation and coding; BS: base station; LT: lower bound of total utility; MCS: modulation and coding scheme; MSS: mobile subscriber station; SNR: signal-to-noise ratio; SVC: scalable video coding; TUMP: total utility maximization problem. Acknowledgements This article was supported in part by the National Science Council of the ROC, under Grants NSC -97-2221-E-009-048-MY3 and NSC-97-2221-E-009-049MY3.
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Competing interests The authors declare that they have no competing interests. Received: 27 October 2010 Accepted: 9 July 2011 Published: 9 July 2011 References 1. 4G Coverage, Sprint, http://www.sprint.com/ 2. VMAX, http://www.vmax.net.tw/ 3. IEEE Computer Society and IEEE Microwave Theory and Techniques Society, IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems. IEEE Standard 802.16e-2005 (2005) 4. IEEE Computer Society and IEEE Microwave Theory and Techniques Society, IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems. IEEE standard 802.16-2004 (2004) 5. JG Andrews, A Ghosh, R Muhamed, Fundamentals of WiMAX: Understanding Broadband Wireless Networking, 1st edn. (Prentice Hall, New Jersey, 2007) 6. M Hauge, Ø Kure, Multicast in 3G networks: employment of existing IP Multicast protocols in UMTS, International Workshop on Wireless Mobile Multimedia (WoWMoM), Atlanta, USA, Sept. 2002 7. N Jindal, ZQ Luo, Capacity limits of multiple antenna multicast, in IEEE International Symposium on Information Theory (ISIT), (Seattle, USA, July 2006) 8. H Schwarz, D Marpe, T Wiegand, Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Trans. Circuits Syst Video Technol. 17(9), 1103–1129 (2007) 9. S Shenker, Fundamental design issues for the future internet. IEEE J Sel Areas Commun. 13(7), 1176–1188 (1995) 10. L Shi, C Liu, B Liu, Network utility maximization for triple-play services. Comput. Commun. 31, 2257–2269 (2008). doi:10.1016/j.comcom.2008.02.016 11. J Liu, B Li, YT Hou, I Chlamtac, Dynamic layering and bandwidth allocation for multisession video broadcasting with general utility functions, in The 22th IEEE International Conference on Computer Communications (IEEE INFOCOM), San Francisco, USA, April 2003 12. WH Kuo, T Liu, W Liao, Utility-based resource allocation for layer-encoded IPTV multicast in IEEE 802.16 (WiMAX) wireless networks, in IEEE International Conference on Communications (ICC), Glasgow, Scotland, June 2007 13. P Li, H Zhang, B Zhao, S Rangarajan, Scalable video multicast in multicarrier wireless data systems, in The 17th IEEE International Conference On Network Protocols (ICNP), Princeton, USA, October 2009 14. H Chi, C Lin, Y Chen, C Chen, Optimal rate allocation for scalable video multicast over WiMAX, in IEEE International Symposium on Circuits and Systems (ISCAS), Seattle, USA, May 2008 15. J Shi, D Qu, G Zhu, Utility maximization of layered video multicasting for wireless systems with adaptive modulation and coding, in IEEE International Conference on Communications (ICC), Istanbul, Turkey, June 2006 16. S Deb, S Jaiswal, K Nagaraj, Real-time video multicast in WiMAX networks, in The 27th IEEE Conference on Computer Communications (IEEE INFOCOM), Phoenix, USA, April 2008 17. C Huang, P Wu, S Lin, J Hwang, Layered video resource allocation in mobile WiMAX using opportunistic multicasting, in IEEE Wireless Communication and Networking Conference (WCNC), Budapest, Hungary, April 2009 18. CS Hwang, Y Kim, An adaptive modulation method for multicast communications of hierarchical data in wireless networks, in IEEE international conference on communications (ICC), New York, USA, April 2002 19. M Shabany, K Navaie, Es Sousa1, A utility-based downlink radio resource allocation for multiservice cellular DS-CDMA networks. EURASIP J Wirel Commun Netw. 2007(1) (2007) 20. J Kim, D Cho, Enhanced adaptive modulation and coding schemes based on multiple channel reportings for wireless multicast systems, in IEEE Vehicular Technology Conference (VTC 2005 Fall), Dallas, USA, Sept. 2005 21. H Wang, HP Schwefel, TS Toftrgaard, History-based adaptive modulation for a downlink multicast channel in OFDMA systems, in IEEE Wireless Communication and Networking Conference (WCNC), Las Vegas, USA, 2008 22. H Kellerer, U Pferschy, D Psdinger, Knapsack Problem, (Springer, Berlin, 2004)
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